{
    iF(F){ # Results verification- Para:
        
        library("pacman")
        p_load("dplyr")
        p_load("plyr")
        p_load("ggplot2")
        p_load("stringr")
        p_load("reshape2")
        p_load("PMCMR")
        p_load("ggforce")
        p_load(openxlsx)
        p_load(Loafer)
        
        obj.para <- get_pic_with_labels(input_df = iris[,1,drop = F],
                                        # input_df$Sepal.Length <- rnorm(nrow(input_df)),
                                        group_info_df = iris[,5,drop = F],
                                        group_index = 1,
                                        group_levels_in_order_C = group_info_df[,1] %>% unique(),
                                        test_pattern  = "para" ,
                                        # test_pattern  = "wise" ,
                                        # test_pattern  = "non-para" %>% tolower(),
                                        p_for_label = "p_all",
                                        step = 0.05,
                                        draw_pics = F, 
                                        position_by = "max single value")
        
        
        
        colnames(iris)[1]
        fit.aov <- aov(Sepal.Length ~ Species, data = iris)
        p.all.para <- summary(fit.aov) %>% `[[`(., 1) %>%  `[`(., 1, 5) ## p.all by aov
        p.all.para == obj.para@result_df$p_all[1] ## check the aov result
        ## Check the t test p value.
        p_raw <- pairwise.t.test(x = iris$Sepal.Length, g = iris$Species,
                                 paired = F, p.adjus.method = "none")
        obj.para@result_df$pairwise_p.raw %in% p_raw$p.value
        ## Check the post-hoc p-value.
        Tukeyresult <- TukeyHSD(fit.aov, "Species")
        tukey.p.para <- Tukeyresult$Species %>% `[`(., ,4)
        tukey.p.para == obj.para@result_df$pairwise_p.adj
        
        
        
        # Results verification- Non-Para:
        obj.nonpara <- get_pic_with_labels(input_df = iris[,1,drop = F],
                                           # input_df$Sepal.Length <- rnorm(nrow(input_df)),
                                           group_info_df = iris[,5,drop = F],
                                           group_index = 1,
                                           group_levels_in_order_C = group_info_df[,1] %>% unique(),
                                           test_pattern  = "non-para" ,
                                           # test_pattern  = "wise" ,
                                           # test_pattern  = "non-para" %>% tolower(),
                                           p_for_label = "p_all",
                                           step = 0.05,
                                           draw_pics = F, 
                                           position_by = "max single value")
        
        fit <- kruskal.test(iris$Sepal.Length, iris$Species, na.rm = F)
        p_all <- fit$p.value
        p_all == obj.nonpara@result_df$p_all[1]
        ## Check the raw p by non-para.
        res <- pairwise.wilcox.test(x = iris$Sepal.Length, g = iris$Species, p.adjust.method = "none")
        obj.nonpara@result_df$pairwise_p.raw %in% res$p.value
        ## Check the post-hoc P-value by non-para.
        posthoc_result <-  posthoc.kruskal.nemenyi.test( x = iris$Sepal.Length, g = iris$Species, dist = c("Tukey"))
        obj.nonpara@result_df$pairwise_p.adj %in% posthoc_result$p.value
    }
}